3D object detection aims to obtain accurate 3D information by multiple sensors, which is one of the core techniques in autonomous driving. With the development of deep learning, many large-scale point clouds datasets [1]}, [2]}, [3]} have been published and have greatly promoted the ability of environment perception for unmanned vehicles. However, the existing 3D object detection methods rely on large-scale annotated point clouds for model training. However, it needs to pay a lot of money and energy to collect and annotate the datasets. How to design the self-supervised learning task with the available large amount of unannotated point clouds is of great significance to the perceptual ability of autonomous driving.
